| # gpt6_b1k — standalone Task 01 policy |
| |
| Target: mean official final Q **above 0.7** on all 20 public `picking_up_trash` instances (301–320). **Complete frozen evaluation: mean Q = 0.766667, 13/20 successes; target achieved.** See [EVALUATION.md](EVALUATION.md) for all 20 official results. |
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| The policy runs locally using NumPy, SciPy, OpenCV, static R1Pro kinematics, RGB, linear depth, and proprioception. The fixed-scene prior mode additionally uses local Torch/ALIKED/LightGlue models and precomputed map assets. The visual navigator also uses camera-relative calibration supplied in official evaluation observations. It makes no Codex/backend/LLM calls and needs no API key. The runner selects a free loopback port for each launch; its localhost websocket connects the official evaluator to the local policy. Use `--port` only when a fixed port is needed. The policy never imports OmniGibson or reads scene-state files; the separate official evaluator runs the simulator. Privileged object/world geometry is confined to the separate `debug/` tools. |
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| ## Measured status |
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| The default `prior` policy completed all 20 official instances, with mean final Q **0.766667** and **13/20 official successes**. Results include every failed episode, using one frozen policy, seed 0, the official horizon, and full RGB-D observations. The complete table, infrastructure-resume provenance, and remaining failures are in [EVALUATION.md](EVALUATION.md). |
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| The policy uses a user-authorized static scene map, 57 reference views, local ALIKED/LightGlue localization, short RGB-D tracking, live depth obstacle checks, and reachability-aware grasp/placement stances. The map is prepared once. Dynamic cans and bin are detected from current observations; their positions are not stored in the prior. See [PRIOR_DESIGN.md](PRIOR_DESIGN.md). |
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| Earlier targeted tests and failed candidates are recorded separately in [EXPERIMENTS.md](EXPERIMENTS.md). Their scores are not combined with the completed frozen evaluation. This result demonstrates the agreed known-scene mean-Q target, not success on every instance or unseen-scene generalization. |
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| ## Existing laptop environment |
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| ```bash |
| cd /home/eai/yixin/gpt6_b1k |
| source env.sh |
| ``` |
| |
| Defaults use the existing `behavior-392dev` environment, the clean BEHAVIOR v3.9.2 checkout at `/home/eai/code/BEHAVIOR_1K_v392`, and assets at `/home/eai/code/b1k/datasets`. For another computer, set `B1K_PYTHON`, `B1K_REPO`, and `OMNIGIBSON_DATA_PATH` before sourcing `env.sh`. OmniGibson, Isaac Sim, licensed assets, and the GPU driver must already be installed. No installation or simulator modification is performed by this package. |
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| ## Copy on 5080-1 |
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| `ssh 5080-1` reaches `TRT-EAI-OLDLAB-1`. The project destination is `/shared_work/markhsp/gpt6_b1k`. There, use `source env_5080.sh` to select the existing `behavior2026` Python environment, official v3.9.2 export, and shared datasets. Simulator development results reported here were produced on the laptop; the remote copy does not imply remote GPU validation. Follow the site's existing GPU allocation procedure before launching simulation there. |
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| ## Short development trial |
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| ```bash |
| "$B1K_PYTHON" run.py --navigation prior --indices 0 --max-steps 1200 --output runs/pilot01 |
| ``` |
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| Explicit time-limited runs are labeled diagnostic. Each run stores a source fingerprint, commands, action logs, RGB-D/proprio snapshots, and official evaluator outputs. Use a new output directory after changing code. Repeating a command with identical code/settings skips completed instances. |
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| `prior` is the CLI default. The older `visual` and `validated` modes remain available for explicit comparison. Every mode runs without an external model service. |
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| ## Full evaluation command (expensive) |
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| ```bash |
| "$B1K_PYTHON" run.py --navigation prior --all --output runs/frozen20 |
| ``` |
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| This runs 20 instances sequentially in **one official evaluator process**, reusing the loaded simulator. It uses the official default horizon, default robot/controller settings, seed 0, and full RGB-D wrapper. Results appear under `runs/frozen20/batch_000/evaluation/`; policy recordings are under `batch_000/policy/`. A source snapshot is stored with each run. Progress summaries update after each completed instance. Full video encoding is optional with `--write-video`; sampled RGB-D observations and action logs are always retained. This follows the official evaluator's default video setting and does not change its observations or metrics. The target is only declared achieved if all 20 completed with mean final Q > 0.7 and no diagnostic horizon override. Resuming runs only the missing indices in a new batch and retains failed-task scores rather than selecting better retries. |
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| ## Inspect without restarting simulation for every experiment |
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| ```bash |
| "$B1K_PYTHON" debug/session.py --instance 301 --output debug/lab301 |
| "$B1K_PYTHON" debug/analyze.py debug/lab301/initial |
| ``` |
| |
| The development lab accepts JSON commands in `debug/lab301/command.json`, e.g. `{"op":"run","name":"scan01","scan_only":true,"steps":1200,"stop_phase":"inspection_complete"}`. It reuses the loaded scene, resets for each experiment, and writes sensor recordings and separate privileged diagnostic geometry. End with `{"op":"quit"}`. These commands and geometry are never used by `run.py` or the final policy server. |
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| ## Checks |
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| ```bash |
| "$B1K_PYTHON" -m pytest -q tests |
| ``` |
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| Motion/sensor helpers were adapted from the user-provided `5080-1:/shared_work/markhsp/gpt6_behavior_solution` reference, via its preserved r21 source. Robot geometry and a robot-only spare-arm posture are reused; no task2 scene coordinates or action replay are used. |
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